Mark Girolami is a Computational Statistician having ten years experience as a Chartered Engineer within IBM. In March 2019 he was elected to the Sir Kirby Laing Professorship of Civil Engineering (1965) within the Department of Engineering at the University of Cambridge where he also holds the Royal Academy of Engineering Research Chair in Data Centric Engineering. Girolami takes up the Sir Kirby Laing Chair upon the retirement of Professor Lord Robert Mair. Professor Girolami is a fellow of Christ’s College Cambridge.
Prior to joining the University of Cambridge Professor Girolami held the Chair of Statistics in the Department of Mathematics at Imperial College London. He was one of the original founding Executive Directors of the Alan Turing Institute the UK’s national institute for Data Science and Artificial Intelligence, after which he was appointed as Strategic Programme Director at Turing, where he established and led the Lloyd’s Register Foundation Programme on Data Centric Engineering. Since October 2021 he serves as the Chief Scientist of the Alan Turing Institute.
Professor Girolami is an elected fellow of the Royal Society of Edinburgh, he was an EPSRC Advanced Research Fellow (2007-2012), an EPSRC Established Career Research Fellow (2012-2018), and a recipient of a Royal Society Wolfson Research Merit Award.
He delivered the IMS Medallion Lecture at the Joint Statistical Meeting 2017, and the Bernoulli Society Forum Lecture at the European Meeting of Statisticians 2017.
In 2020 Professor Girolami delivered the BCS and IET Turing Talk in London, Manchester, and Belfast.
Professor Girolami currently serves as the Editor-in-Chief of Statistics and Computing and the new open access journal Data Centric Engineering published by Cambridge University Press.
Narges is an assistant professor in the Departments of Population Health and Radiology conducting research in the Center for Healthcare Innovation and Delivery Science (CHIDS), and a member of its Predictive Analytics Unit.
Her lab's research is focused on the intersection of machine learning, artificial intelligence, and medicine. Using millions of records in the Electronic Health Records database at NYU Langone, as well as hundreds of thousands of imaging and millions of genomic data points, they focus on a number of important topics, including but not limited to: prediction of upcoming preventable conditions and events using machine learning and data science, discovery of disease subtypes using radiology and pathology imaging and electronic records, discovery of existing but undiagnosed medical conditions using electronic health records, and, the discovery of biomarkers and factors associated with important outcomes, etc.
Mathilde is currently a Research Scientist at Google interested in application of deep learning to vision and multi-modal data without using human supervision.
Before that she was a Ph.D. student at Inria Grenoble and Fair Paris (Meta AI), working on large-scale self-supervised representation learning for vision.
She won the annual ELLIS PhD award in 2022 and her works appeared in NeurIPS, ECCV, ICCV, and TPAMI.
Aasa Feragen is a rogue mathematician who has worked at the intersection of machine learning and medical imaging since 2009. She is also a professor at the Technical University of Denmark. Her MSc and PhD in mathematics are both from the University of Helsinki, following which she held postdocs at the University of Copenhagen, and the MPI for Intelligent Systems in Tübingen.